Touch may require movements to be made with fingertips in order to sense information about what the fingers are touching. The nature of these movements may be optimized to extract the tactile properties of an object that may be useful for identifying the object. Experimental psychologists have observed a number of useful types of exploratory movements that humans make when identifying objects by touch, such as hefting, enclosing, applying pressure, and sliding. (Lederman, S J, and R L Klatzky. 1987. “Hand Movements: a Window Into Haptic Object Recognition.” Cognitive Psychology 19: 342-368.). However, even within these discrete sets of movements, there may be many ways in which these movements can be executed to collect information. For instance, different combinations of forces and sliding trajectories could be made when performing a sliding movement. Given the large number of possible movements and variations in parameters, it may be impractical to perform every possible movement to collect every piece of information before identifying what is being touched. Similar problems may arise during any type of diagnostic task when it may be impractical to collect all information before making a decision. For example, the definitive diagnosis of a disease given an initial set of symptoms could benefit from a very large number of possible tests, each of which takes a significant amount of time and money to perform. Physicians use a subjective process called differential diagnosis to estimate the probability of each possible diagnosis and the potential of each available test to differentiate among them. It would be advantageous to have an objective method to determine the most efficient sequence of tests to arrive at a final diagnosis.
Human skin contains a variety of neural transducers that sense mechanical strain, vibrations, and thermal information (Jones, L A, and S J Lederman. 2006. Human Hand Function. New York, N.Y.: Oxford University Press, USA.; Vallbo, Å B, and R S Johansson. 1984. “Properties of Cutaneous Mechanoreceptors in the Human Hand Related to Touch Sensation.” Human Neurobiology 3 (1): 3-14.). The skin and its sensory transducers are highly evolved and specialized in structure, and the glabrous skin found on the palmar surface of the human hand, and in particular the fingertip, may possess a higher density of cutaneous receptors than the hairy skin on the rest of the body (Vallbo, Å B, and R S Johansson. 1978. “The Tactile Sensory Innervation of the Glabrous Skin of the Human Hand.” In Active Touch, the Mechanism of Recognition of Objects by Manipulation, edited by G Gordon, 29-54. Oxford: Pergamon Press Ltd.; Johansson, R S, and Å B Vallbo. 1979. “Tactile Sensibility in the Human Hand: Relative and Absolute Densities of Four Types of Mechanoreceptive Units in Glabrous Skin.” Journal of Physiology 286 (1): 283.). A device that mimics these sensory capabilities has been described in a form factor that has size, shape and mechanical properties similar to a human fingertip (U.S. Pat. Nos. 7,658,110, 7,878,075, 8,181,540 and 8,272,278). Other tactile sensors designed to replicate human touch have been described in a number of literature reviews covering several decades of research (Nicholls, H R, and M H Lee. 1989. “A Survey of Robot Tactile Sensing Technology.” International Journal of Robotics Research 8 (3): 3-30.; Howe, R D. 1994. “Tactile Sensing and Control of Robotic Manipulation.” Advanced Robotics 8 (3): 245-261.; Lee, M H, and H R Nicholls. 1999. “Tactile Sensing for Mechatronics—a State of the Art Survey.” Mechatronics 9: 1-31.; Dahiya, R S, G Metta, M Valle, and G Sandini. 2010. “Tactile Sensing—From Humans to Humanoids.” IEEE Transactions on Robotics 26 (1): 1-20.).
Another approach is artificial texture recognition with tactile sensors (Tada, Y, K Hosoda, and M Asada. 2004. “Sensing Ability of Anthropomorphic Fingertip with Multi-Modal Sensors.” In Proc. IEEE International Conference on Intelligent Robots and Systems, 1005-1012.; Mukaibo, Y, H Shirado, M Konyo, and T Maeno. 2005. “Development of a Texture Sensor Emulating the Tissue Structure and Perceptual Mechanism of Human Fingers.” In Proc. IEEE International Conference on Robotics and Automation, 2565-2570. IEEE.; Hosoda, K, Y Tada, and M Asada. 2006. “Anthropomorphic Robotic Soft Fingertip with Randomly Distributed Receptors.” Robotics and Autonomous Systems 54 (2): 104-109.; de Boissieu, F, C Godin, B Guilhamat, D David, C Serviere, and D Baudois. 2009. “Tactile Texture Recognition with a 3-Axial Force MEMS Integrated Artificial Finger.” In Proc. Robotics: Science and Systems, 49-56.; Sinapov, J, and A Stoytchev. 2010. “The Boosting Effect of Exploratory Behaviors.” In Proc. Association for the Advancement of Artificial Intelligence, 1613-1618.; Giguere, P, and G Dudek. 2011. “A Simple Tactile Probe for Surface Identification by Mobile Robots.” IEEE Transactions on Robotics 27 (3): 534-544.; Oddo, C M, M Controzzi, L Beccai, C Cipriani, and M C Carrozza. 2011. “Roughness Encoding for Discrimination of Surfaces in Artificial Active-Touch.” IEEE Transactions on Robotics 27 (3): 522-533.; Jamali, N, and C Sammut. 2011. “Majority Voting: Material Classification by Tactile Sensing Using Surface Texture.” IEEE Transactions on Robotics 27 (3): 508-521.; Sinapov, J, V Sukhoy, R Sahai, and A Stoytchev. 2011. “Vibrotactile Recognition and Categorization of Surfaces by a Humanoid Robot.” IEEE Transactions on Robotics 27 (3): 488-497.; Chu, V, I McMahon, L Riano, C G McDonald, Q He, J M Perez-Tejada, M Arrigo, et al. 2013. “Using Robotic Exploratory Procedures to Learn the Meaning of Haptic Adjectives.” In Proc. IEEE International Conference on Robotics and Automation.). The sliding movements humans make when identifying surface texture (Lederman, S J, and R L Klatzky. 1987. “Hand Movements: a Window Into Haptic Object Recognition.” Cognitive Psychology 19: 342-368.) may be executed with these sensors over a number of textures to identify which characteristics make them unique. Various approaches to producing these movements have been explored, including using anthropomorphic hands (Tada, Y, K Hosoda, and M Asada. 2004. “Sensing Ability of Anthropomorphic Fingertip with Multi-Modal Sensors.” In Proc. IEEE International Conference on Intelligent Robots and Systems, 1005-1012.; Hosoda, K, Y Tada, and M Asada. 2006. “Anthropomorphic Robotic Soft Fingertip with Randomly Distributed Receptors.” Robotics and Autonomous Systems 54 (2): 104-109.; Oddo, C M, M Controzzi, L Beccai, C Cipriani, and M C Carrozza. 2011. “Roughness Encoding for Discrimination of Surfaces in Artificial Active-Touch.” IEEE Transactions on Robotics 27 (3): 522-533.; Jamali, N, and C Sammut. 2011. “Majority Voting: Material Classification by Tactile Sensing Using Surface Texture.” IEEE Transactions on Robotics 27 (3): 508-521.; Chu, V, I McMahon, L Riano, C G McDonald, Q He, J M Perez-Tejada, M Arrigo, et al. 2013. “Using Robotic Exploratory Procedures to Learn the Meaning of Haptic Adjectives.” In Proc. IEEE International Conference on Robotics and Automation.), 2-axis plotting machines (de Boissieu, F, C Godin, B Guilhamat, D David, C Serviere, and D Baudois. 2009. “Tactile Texture Recognition with a 3-Axial Force MEMS Integrated Artificial Finger.” In Proc. Robotics: Science and Systems, 49-56.), robotic arms (Sinapov, J, V Sukhoy, R Sahai, and A Stoytchev. 2011. “Vibrotactile Recognition and Categorization of Surfaces by a Humanoid Robot.” IEEE Transactions on Robotics 27 (3): 488-497.), or manual sliding (Giguere, P, and G Dudek. 2011. “A Simple Tactile Probe for Surface Identification by Mobile Robots.” IEEE Transactions on Robotics 27 (3): 534-544.). Previous studies employed a fixed exploration sequence for collecting data, which, after processing, was fed into a machine learning classifier that sought to identify the texture. One exception was (Jamali, N, and C Sammut. 2011. “Majority Voting: Material Classification by Tactile Sensing Using Surface Texture.” IEEE Transactions on Robotics 27 (3): 508-521.), who repeated the same sliding movement until the classification reached a desired confidence.
Using additional exploratory movements has been demonstrated to improve performance (Sinapov, J, V Sukhoy, R Sahai, and A Stoytchev. 2011. “Vibrotactile Recognition and Categorization of Surfaces by a Humanoid Robot.” IEEE Transactions on Robotics 27 (3): 488-497.). However, executing every possible movement to gain all information about an object may be impractical, so these systems were restricted to a small number of preprogrammed exploratory movements. This approach may only provide marginal performance accuracies when using a small number of highly distinctive surfaces that would be trivial for a human observer to discriminate. Examples of classification performance in previous literature include: 62% over 10 textures (de Boissieu, F, C Godin, B Guilhamat, D David, C Serviere, and D Baudois. 2009. “Tactile Texture Recognition with a 3-Axial Force MEMS Integrated Artificial Finger.” In Proc. Robotics: Science and Systems, 49-56.), 89.9-94.6% over 10 textures (Giguere, P, and G Dudek. 2011. “A Simple Tactile Probe for Surface Identification by Mobile Robots.” IEEE Transactions on Robotics 27 (3): 534-544.), 95% over 20 textures (Sinapov, J, V Sukhoy, R Sahai, and A Stoytchev. 2011. “Vibrotactile Recognition and Categorization of Surfaces by a Humanoid Robot.” IEEE Transactions on Robotics 27 (3): 488-497.), 97.6% over 3 textures (Oddo, C M, M Controzzi, L Beccai, C Cipriani, and M C Carrozza. 2011. “Roughness Encoding for Discrimination of Surfaces in Artificial Active-Touch.” IEEE Transactions on Robotics 27 (3): 522-533.), and 95% over 8 textures (Jamali, N, and C Sammut. 2011. “Majority Voting: Material Classification by Tactile Sensing Using Surface Texture.” IEEE Transactions on Robotics 27 (3): 508-521.).
Loeb et al., 2011, (Loeb, G E, G A Tsianos, J A Fishel, N Wettels, and S Schaal. 2011. “Understanding Haptics by Evolving Mechatronic Systems.” Progress in Brain Research 192: 129-144.), suggested the general desirability of selecting exploratory movements incrementally according to the most likely identity of the object being explored but, provided no examples or methods to do so.